Utility of the revised Level of Service Inventory (LSI-R) in predicting recidivism after long-term incarceration.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Assessing an inmate's risk for recidivism may become more challenging as the length of incarceration increases. Although the population of Long-Term Inmates (LTIs) is burgeoning, no risk assessment tools have been specifically validated for this group. Based on a sample of 1,144 inmates released in a state without parole, we examine the utility of the Level of Service Inventory-Revised (LSI-R) in assessing risk of general and violent felony recidivism for LTIs (n = 555). Results indicate that (a) the LSI-R moderately predicts general, but not necessarily violent, recidivism, and (b) this predictive utility is not moderated by LTI status, and is based in part on ostensibly dynamic risk factors. Implications for informing parole decision-making and risk management for LTIs are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it